Photoprobes for identifying potential anti-depressant and anti-anxiety medication

描述(由申请人提供)：在分子水平上理解抗抑郁药如何与其药物靶标相互作用存在着根本的差距。这一差距的持续存在是合理设计或开发精神疾病新疗法方面的一个重要问题。我们研究的长期目标是了解临床上相关的抗抑郁化合物如何在分子水平上与其生化目标相互作用。本申请的目的是开发和利用基于西酞普兰和氟西汀的光亲和探针，这两种化合物在治疗抑郁症和其他精神疾病方面具有临床意义，以绘制它们在5-羟色胺转运体(SERT)中的相应结合位置和姿势。中心假设是这些选择性5-羟色胺再摄取抑制剂(SSRI)可以通过光反应基团和生物正交标记进行衍生化，以应用点击化学蛋白质组学技术。在特定的交联后，标记的SERT将被蛋白质降解和大量指纹图谱，并将在LC-MS/MS研究中分析交联肽，以确定每个探针的特定共价结合位置。我们的新方法将光亲和标记与灵敏质谱学(MS)相结合，以直接确定光探针与SERT的相互作用部位。识别这些位点将允许我们的分子建模研究的精细化，这些研究映射了SERT中的配体结合姿势和位点。这项拟议研究的基本原理是，一旦知道SSRI如何与SERT相互作用，就可以合理地将先导化合物作为一系列精神疾病的潜在候选药物来操纵。中心假设将通过追求两个具体目标来检验：1)开发用于标记SERT的基于SSRI的光探针库；2)鉴定SERT中光探针的不可逆附着位置。在第一个目标下，西酞普兰和氟西汀将进行结构修饰，以包含光反应基团(例如，二苯甲酮、二苯甲酮)和可点击标签(例如，末端炔、脂肪族叠氮)，然后进行SERT药理学评价，以确定适合蛋白质组表征的光探针。在第二个目标下，SERT光亲和标记结合MS将为Aim 1中开发的光探针识别特定的药物-蛋白质接触。所有结果将与SERT分子模拟相结合，以完善我们的计算模型，并准确地映射西酞普兰和氟西汀在转运体中的配体结合位置和位置。这项研究具有创新性，因为它使用串联光亲和标记-生物正交结合化学蛋白质组学方法来直接定位SERT中SSRI相互作用的位置。这项拟议的研究具有重要意义，因为它有望垂直推进和扩大我们对SSRI作为临床相关抗抑郁化合物，如何在分子水平上与其主要药物靶点相互作用的理解。最终，这些知识有可能指导未来对许多SERT相关疾病(如抑郁症、焦虑症、自闭症、强迫症)候选药物的配体优化，并为计算机辅助药物发现努力(即虚拟/电子筛选、基于结构的药物设计)完善SERT分子模型。

DESCRIPTION (provided by applicant): There is a fundamental gap in understanding how anti-depressants interact with their drug targets at the molecular level. Continued existence of this gap represents an important problem in terms of rationally designing or developing new therapeutics for mental illnesses. The long-term goal of our research is to understand how clinically relevant anti-depressant compounds interact with their biochemical targets at the molecular level. The objective of this application is to develop and utilize photoaffinity probes based on citalopram and fluoxetine, clinically significant compounds for treating depression and other mental illnesses, to map their corresponding binding sites and poses within the serotonin transporter (SERT). The central hypothesis is that these selective serotonin reuptake inhibitors (SSRIs) can be derivatized with a photoreactive group and a bioorthogonal tag for application of click chemistry proteomic techniques. After specific crosslinking, tagged SERT will be proteolyzed and mass fingerprinted, and crosslinked peptides will be analyzed in LC-MS/MS studies to identify the specific sites of covalent attachment of each probe. Our novel approach couples photoaffinity labeling with sensitive mass spectrometry (MS) to directly identify sites of interaction of photoprobes with the SERT. Identification of these sites will allow for refinement o our molecular modeling studies that map ligand-binding poses and sites within SERT. The rationale that underlies the proposed research is that, once it is known how SSRIs interact with the SERT, lead compounds can then be rationally manipulated as potential drug candidates for a host of mental illnesses. The central hypothesis will be tested by pursuing two specific aims: 1) Development of a library of SSRI-based photoprobes for labeling SERT; and 2) Identification of the irreversible attachment sites for the photoprobes within SERT. Under the first aim, citalopram and fluoxetine will be structurally modified to contain a photoreactive group (e.g., ary azide, benzophenone) and clickable tag (e.g., terminal alkyne, aliphatic azide), followed by SERT pharmacological evaluation to identify suitable photoprobes for proteomic characterization. Under the second aim, SERT photoaffinity labeling coupled with MS will identify specific drug-protein contacts for photoprobes developed in Aim 1. All results will be coupled with SERT molecular modeling in order to refine our computational models and accurately map the ligand-binding poses and sites for citalopram and fluoxetine within the transporter. The research is innovative because it uses a tandem photoaffinity labeling-bioorthogonal conjugation chemical proteomics approach to directly map the sites of SSRI interactions in SERT. The proposed research is significant, because it is expected to vertically advance and expand our understanding of how SSRIs, as clinically relevant anti-depressant compounds, interact with their major drug target at the molecular level. Ultimately, such knowledge has the potential to guide future ligand optimization of drug candidates for numerous SERT-implicated disorders (e.g., depression, anxiety, autism, obsessive- compulsive disorder) and refine SERT molecular models for computer-aided drug discovery efforts (i.e., virtual / in silico screening, structure-based drug design).